(topic07_intro)=

# Topic 7. Unsupervised Learning: Principal Component Analysis and Clustering

```{figure} /_static/img/topic7-teaser.jpg
:name: topic5-teaser
:width: 200px
```

Here we turn to the vast topic of unsupervised learning; it’s about the cases when we have data but it is unlabeled with no target feature to predict like in classification/regression tasks. Most of the data out there is unlabeled, and we need to be able to make use of it. We discuss only 2 types of unsupervised learning tasks -- clustering and dimensionality reduction.

## Steps in this block

1\. Read the [article](topic07) (same in a form of a [Kaggle Notebook](https://www.kaggle.com/kashnitsky/topic-7-unsupervised-learning-pca-and-clustering));

2\. Watch a video lecture series coming in 2 parts:
 - [Principal Component Analysis](https://youtu.be/-AswHf7h0I4);
 - [Clustering](https://youtu.be/eVplCo-w4XE);

3\. Complete [demo assignment 7](assignment07) (same as a [Kaggle Notebook](https://www.kaggle.com/kashnitsky/assignment-7-unupervised-learning)) where you analyze data coming from mobile phone accelerometers and gyroscopes to cluster people into different types of physical activities;

4\. Check out the [solution](assignment07_solution) (same as a [Kaggle Notebook](https://www.kaggle.com/kashnitsky/a7-demo-unsupervised-learning-solution)) to the demo assignment (optional);

5\. Complete [Bonus Assignment 7](bonus07), where we walk you through Sklearn built-in implementations of dimensionality reduction and clustering methods and apply these techniques to the popular “faces” dataset (optional, available under Patreon ["Bonus Assignments" tier](https://www.patreon.com/ods_mlcourse)).